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A Multi-Period Multiple Objective Uncertain Programming Model to Allocate Order for Supplier Selection Problem

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2016年第6期33卷 1650045-1650045页

作者： Eydi, Alireza Fazli, Leyla Univ Kurdistan Fac Engn Sanandaj Iran Univ Kurdistan Dept Ind Engn Sanandaj Iran

As a basic part of organizations' logistics management, purchasing function has supplier selection as one of its main responsibilities. One of the main objectives a buyer follows in supplier selection is to determine optimal quota to be allocated to each supplier. How to allocate orders to different suppliers is as important task as it may affect efficiency of the whole chain. Also, due to variations in real-world business environment, order allocation process is always associated with uncertainties that make it complicated. Therefore, a three-stage integrated framework with environmental uncertainties considered is proposed to allocate orders;this framework can determine qualified suppliers to whom it assigns optimal quota. Considering multi-period purchases and uncertainties, this framework presents a multi-objective nonlinear programming model to determine optimal quota to be allocated to each qualified supplier within each specified period. In order to have the order allocation process closer to real-world cases while increasing the reliability of the obtained solutions, time value of money, inflation, transportation modes, supplier's profit, and pricing strategy are considered in this model. According to uncertain structure of the proposed model, a solution strategy is proposed to convert this model into a single-objective deterministic model. Then, the resulted single-objective deterministic model is solved by proposing three evolutionary metaheuristic algorithms based on cuckoo optimization algorithm and imperialist competitive algorithm. Finally, a sample problem is presented together with some statistical tests and sensitivity analyses to assess and examine the proposed framework.

关键词： Supplier selection order allocation uncertainty multi-objective nonlinear programming model multi-period cuckoo optimization algorithm imperialist competitive algorithm

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Toward reliable calibration of aquifer hydrodynamic parameters: characterizing and optimization of arid groundwater system using swarm intelligence optimization algorithm

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ARABIAN JOURNAL OF GEOSCIENCES 2016年第18期9卷 719-719页

作者： Sadeghi-Tabas, S. Akbarpour, A. Pourreza-Bilondi, M. Samadi, S. IUST Dept Civil Engn Tehran Iran Univ Birjand Dept Water Engn Birjand Iran Univ Birjand Dept Civil Engn Birjand Iran Univ South Carolina Dept Civil & Environm Engn Columbia SC 29208 USA

Despite advanced development in computational techniques, the issue of how to adequately calibrate and minimize misfit between system properties and corresponding measurements remains a challenging task in groundwater modeling. Two important features of the groundwater regime, hydraulic conductivity (k) and specific yield (S-y), that control aquifer dynamic vary spatially within an aquifer system due to geologic heterogeneity. This paper provides the first attempt in using an advanced swarm-intelligence-based optimization algorithm (cuckoo optimization algorithm, COA) coupled with a distributed hydrogeology model (i.e., MODFLOW) to calibrate aquifer hydrodynamic parameters (S-y and k) over an arid groundwater system in east Iran. Our optimization approach was posed in a single-objective manner by the trade-off between sum of absolute error and the adherent swarm optimization approach. The COA optimization algorithm further yielded both hydraulic conductivity and specific yield parameters with high performance and the least error. Estimation of depth to water table revealed skillful prediction for a set of cells located at the middle of the aquifer system whereas showed unskillful prediction at the headwater due to frequent water storage changes at the inflow boundary. Groundwater depth reduced from east toward west and southwest parts of the aquifer because of extensive pumping activities that caused a smoothening influence on the shape of the simulated head curve. The results demonstrated a clear need to optimize arid aquifer parameters and to compute groundwater response across an arid region.

关键词： cuckoo optimization algorithm MODFLOW Hydraulic conductivity Specific yield Arid aquifer

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Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization

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APPLIED SOFT COMPUTING 2015年 35卷 43-51页

作者： Elyasigomari, V. Mirjafari, M. S. Screen, H. R. C. Shaheed, M. H. Univ London Sch Engn & Mat Sci London E1 4NS England

This research presents an innovative method for cancer identification and type classification using microarray data. The method is based on gene selection with shuffling in association with optimization based unconventional data clustering. A new hybrid optimization algorithm, COA-GA, is developed by synergizing recently invented cuckoo optimization algorithm (COA) with a more traditional genetic algorithm (GA) for data clustering to select the most dominant genes using shuffling. For gene classification, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are used. Literature suggests that data clustering using traditional approaches such as K-means, C-means and Hierarchical do not have any impact on classification accuracy. This is also confirmed in this investigation. However, results show that optimization based clustering with shuffling increase the classification accuracy significantly. The proposed algorithm (COA-GA) not only outperforms COA, GA and Particle Swarm optimization (PSO) in achieving better classification performance but also reaches a better global minimum with only few iterations. Higher accuracy is observed to have achieved with SVM classifier compared to MLP in all datasets used. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.

关键词： Cancer classification Gene selection Clustering Evolutionary algorithms cuckoo optimization algorithm COA-GA

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Using artificial neural network predictive controller optimized with cuckoo algorithm for pressure tracking in gas distribution network

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2015年第Part3期27卷 1446-1454页

作者： Hadian, Mohsen AliAkbari, Naser Karami, Mahboobeh Petr Univ Technol Dept Instrumentat & Ind Automat Ahvaz Iran Petr Univ Technol Dept Gas Transportat & Proc Engn Ahvaz Iran

In order to model and analyze gas networks, several methods have already been developed and presented. Nevertheless, all these methods have their own specific applications and most of them are very complex and usually contain some errors. In this paper, in an attempt to resolve these problems, an Artificial Neural Network (ANN) has been used to model a gas distribution network. The algorithms utilized for ANN training, such as the gradient descent algorithm, are usually subjected to local minima;in this regard, the new cuckoo optimization algorithm (COA) is used in training the weights of the neural network. However, gas networks are often very large and operate a multitude of distant points, which explains why time delays in these networks are inevitable. Accordingly, in order for all points of the output (pressure) to achieve the desired value, a Model Predictive Controller was used. According to the results achieved, it can be said that the Artificial Neural Network cuckoo optimization algorithm (ANN_COA), in comparison to regular ANN, yields a more suitable performance and is less prone to error. In addition, the MPC controller is faster and suffers from fewer errors compared to the Proportional-Integral-Derivative (PID) controller while also preventing fluctuations in gas system input. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Gas distribution network Model predictive controller Artificial neural network cuckoo optimization algorithm Proportional-integral-derivative controller

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The Effect of Plug-in Electric Vehicles on Harmonic Analysis of Smart Grid

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2015年第6期16卷 559-567页

作者： Heidarian, T. Joorabian, M. Reza, A. Ahvaz Elect Power Distribut Co Ahvaz Iran Shahid Chamran Univ Ahvaz Dept Elect Engn Ahvaz Iran Islamic Azad Univ Dept Mech Engn Ahvaz Branch Ahvaz Iran

In this paper, the effect of plug-in electric vehicles is studied on the smart distribution system with a standard IEEE 30-bus network. At first, harmonic power flow analysis is performed by Newton-Raphson method and by considering distorted substation voltage. Afterward, proper sizes of capacitors is selected by cuckoo optimization algorithm to reduce the power losses and cost and by imposing acceptable limit for total harmonic distortion and RMS voltages. It is proposed that the impact of generated current harmonics by electric vehicle battery chargers should be factored into overall load control strategies of smart appliances. This study is generalized to the different hours of a day by using daily load curve, and then optimum time for charging of electric vehicles batteries in the parking lots are determined by cuckoo optimization algorithm. The results show that injecting harmonic currents of plug-in electric vehicles causes a drop in the voltage profile and increases power loss. Moreover, charging the vehicle batteries has more impact on increasing the power losses rather than the harmonic currents effect. Also, the findings showed that the current harmonics has a great influence on increasing of THD. Finally, optimum working times of all parking lots was obtained for the utilization cost reduction.

关键词： harmonic power flow capacitor sizing electric vehicle cuckoo optimization algorithm distribution network smart grid

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Comparative study of constraint optimizations for automatic voltage regulator

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WORLD JOURNAL OF ENGINEERING 2015年第2期12卷 95-101页

作者： Mehta, Utkal Vachkov, Gancho Univ South Pacific Sch Engn & Phys Suva Fiji

A notable literature is available on optimal design of PID parameters using various evolutionary algorithms. The proper selection of an optimization algorithm is significantly important in finding the robust solution for a given optimization problem. Two most powerful optimization techniques are cuckoo optimization algorithm (COA) and particle swarm optimization (PSO). In this paper, a tight constraint is developed to ensure the less variation in the control input signal and at the same time the overall dynamic performance is not compromised for automatic voltage regulator (AVR) system. Then, using two metaheuristic search algorithms the optimal tune techniques are implemented by imposing the performance index. The presented performance index performs well in the presence of the uncertainties in system parameters. A result is discussed in the simulation study.

关键词： cuckoo optimization algorithm Particle swarm optimization AVR system Control efforts Uncertainties

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Steganography using cuckoo Optimized Wavelet Coefficients 15

Steganography using Cuckoo Optimized Wavelet Coefficients

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3rd International Symposium on Women in Computing and Informatics (WCI)

作者： Singhal, Anuradha Bedi, Punam Univ Delhi Dept Comp Sci Delhi 110007 India

ISBN: (纸本)9781450333610

Secret data hiding has become center of information security research with the enormous growth of the internet. Many researchers are actively working towards increasing the capacity of hidden content, i.e., hiding bigger data/message in a cover without being perceivable. This paper proposes to use cuckoo optimized algorithm for finding best coefficients in the Wavelet Transform domain of an image for embedding secret data to obtain stego image. Huffman encoding is applied on secret data to give encoded bit stream which is embedded in sub bands obtained from wavelet transform of the cover to form stego object. Huffman coding has characteristics of lossless compression which can be used to increase embedding capacity. The algorithm is implemented in Matlab and results are compared with existing LSB steganography and steganography using PSO in wavelet domain in terms of PSNR and SSIM.

关键词： Steganography Discrete Wavelet Transform cuckoo optimization algorithm Security

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Optimal Operation and Output Oscillations Reduction of PEMFC by using an Intelligent Strategy

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2014年第8期9卷 4172-4189页

作者： Sarvi, M. Parpaei, M. Bagheri, H. Kojoori, M. R. Alkaei Imam Khomeini Int Univ Tech & Engn Fac Qazvin Iran

This paper presents a novel maximum power point control of stand-alone proton exchange membrane fuel cell (PEMFC) system using a novel optimization strategy based on the eagle strategy (ES) coupled with cuckoo optimization algorithm (COA) and particle swarm optimization (PSO) in order to track the maximum power point of PEMFC. The proposed method presents a new combination of optimization algorithms. According to this strategy, the advantages of optimization algorithms beside each other lead to reach better results in term of the convergence speed and the accuracy of the optimum operation of the system. Furthermore, the standard deviation of data produced by the proposed method is very lower than other conventional optimization algorithms;PSO and COA algorithms, so it leads to decrease the output power oscillation. To validate these advantages, the proposed tracker applied to the PEMFC system in MATLAB/SIMULINK environment. The simulation results confirm the effectiveness and accuracy of the proposed approach.

关键词： Maximum power point tracking Intelligent optimization algorithm PEMFC Particle swarm optimization cuckoo optimization algorithm

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Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic algorithms Inspired by cuckoo Birds for Training Neural Networks

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algorithmS 2014年第4期7卷 538-553页

作者： Chen, Jeng-Fung Hsieh, Ho-Nien Quang Hung Do Feng Chia Univ Dept Ind Engn & Syst Management Taichung 40724 Taiwan Univ Transport Technol Dept Elect & Elect Engn Hanoi 100000 Vietnam

Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN) with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, cuckoo Search (CS) and cuckoo optimization algorithm (COA) is proposed. In particular, we used previous exam results and other factors, such as the location of the student's high school and the student's gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

关键词： cuckoo search cuckoo optimization algorithm meta-heuristic algorithms neural networks educational institutions

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Morphology Based Iterative Back-Projection for Super-Resolution Reconstruction of Image 2

Morphology Based Iterative Back-Projection for Super-Resolut...

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2nd IEEE International Conference on Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN)

作者： Nayak, Rajashree Harshavardhan, Saka Patra, Dipti Natl Inst Technol Dept Elect Engn Rourkela India

ISBN: (纸本)9781479969869

Super-resolution (SR) reconstruction using iterative back projection (IBP) is a well-known and computationally efficient method for the enhancement of spatial resolution of an image. However, IBP algorithm has some limits in the performance like ringing artifacts in the strong edge area of an image. In this paper, we propose an improved algorithm that modify IBP based SR reconstruction method enable more detail reconstruction and to lessen the ringing artifacts in the image. The current task manages with a constrained optimization of the SR reconstruction problem enforcing the provincially adaptive edge regularization technique using mathematical morphology in the iterative process. Adding to this, cuckoo search and gradient search algorithm combining a hybrid optimization is used to minimize the overall reconstruction error from the high resolution solution of previous IBP model. Experimental results reveal the effectiveness of proposed algorithm it's not only reducing the ringing artifacts, but it is also preserving the edges for getting better resolution and visual perception as compared to the existing state of art methods. It is also clear that this hybrid optimization technique doing something to a greater degree to the corresponding individual search methods.

关键词： Super-resolution Iterative back-projection Mathematical morphology cuckoo optimization algorithm

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